Abstract: A researcher often faces situations wherein interpretation of the topic of discussion in text documents is required. In this context the use of latent topic models have gained popularity. We consider the most widely used latent topic model - the Latent Dirichlet Allocation or LDA model and explore ways to mitigate limitations in topic interpretation and out-of-sample prediction that are concomitant with the conventional LDA method. We propose an approach based on post-processing of LDA output - the “extended” LDA - that sidesteps these drawbacks. We assess the extended LDA model’s topic interpretation and topic recovery performance in two ways: a simulation experiment and human-machine concordance studies. Finally, we empirically demonstrate applications of this approach using Amazon Product reviews and using10-K statements of software and services firms.